Unobtrusive Activity Recognition of Elderly People Living Alone Using Anonymous Binary Sensors and DCNN

Elderly population (over the age of 60) is predicted to be 1.2 billion by 2025. Most of the elderly people would like to stay alone in their own house due to the high eldercare cost and privacy invasion. Unobtrusive activity recognition is the most preferred solution for monitoring daily activities of the elderly people living alone rather than the camera and wearable devices based systems. Thus, we propose an unobtrusive activity recognition classifier using deep convolutional neural network (DCNN) and anonymous binary sensors that are passive infrared motion sensors and door sensors. We employed Aruba annotated open data set that was acquired from a smart home where a voluntary single elderly woman was living inside for eight months. First, ten basic daily activities, namely, Eating, Bed_to_Toilet, Relax, Meal_Preparation, Sleeping, Work, Housekeeping, Wash_Dishes, Enter_Home, and Leave_Home are segmented with different sliding window sizes, and then converted into binary activity images. Next, the activity images are employed as the ground truth for the proposed DCNN model. The 10-fold cross-validation evaluation results indicated that our proposed DCNN model outperforms the existing models with F1-score of 0.79 and 0.951 for all ten activities and eight activities (excluding Leave_Home and Wash_Dishes), respectively.

[1]  Anton Kummert,et al.  Activity recognition using inertial sensors and a 2-D convolutional neural network , 2017, 2017 10th International Workshop on Multidimensional (nD) Systems (nDS).

[2]  Tatsuya Harada,et al.  Recognizing Activities of Daily Living with a Wrist-Mounted Camera , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Koichi Shinoda,et al.  User adaptation of convolutional neural network for human activity recognition , 2017, 2017 25th European Signal Processing Conference (EUSIPCO).

[4]  Bingbing Ni,et al.  First-Person Daily Activity Recognition With Manipulated Object Proposals and Non-Linear Feature Fusion , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[5]  Kevin Kinsella,et al.  Can Populations Age Better, Not Just Live Longer? , 2013 .

[6]  Giovanni Mezzina,et al.  Remote Neuro-Cognitive Impairment Sensing Based on P300 Spatio-Temporal Monitoring , 2016, IEEE Sensors Journal.

[7]  Yi-Ting Chiang,et al.  Interaction‐Feature Enhanced Multiuser Model Learning for a Home Environment Using Ambient Sensors , 2014, Int. J. Intell. Syst..

[8]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[9]  Eui-Nam Huh,et al.  Dementia Wandering Detection and Activity Recognition Algorithm Using Tri-Axial Accelerometer Sensors , 2009, Proceedings of the 4th International Conference on Ubiquitous Information Technologies & Applications.

[10]  Chiew Tong Lau,et al.  Preliminary results of using inertial sensors to detect dementia- related wandering patterns , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[11]  Maureen Schmitter-Edgecombe,et al.  Automated Cognitive Health Assessment Using Smart Home Monitoring of Complex Tasks , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[12]  Diane J. Cook,et al.  Forecasting occurrences of activities , 2017, Pervasive Mob. Comput..

[13]  Yingdong Ma,et al.  Convolutional neural networks (CNN) for indoor human activity recognition using Ubisense system , 2017, 2017 29th Chinese Control And Decision Conference (CCDC).

[14]  William D. Kearns,et al.  A unified grid-based wandering pattern detection algorithm , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[15]  Muttukrishnan Rajarajan,et al.  Activity Recognition in Smart Homes Using Clustering Based Classification , 2014, 2014 22nd International Conference on Pattern Recognition.

[16]  Qi Hao,et al.  Convolution neutral network enhanced binary sensor network for human activity recognition , 2016, 2016 IEEE SENSORS.

[17]  Diane J. Cook,et al.  Recognizing independent and joint activities among multiple residents in smart environments , 2010, J. Ambient Intell. Humaniz. Comput..

[18]  Tan-Hsu Tan,et al.  Device-Free Non-Privacy Invasive Classification of Elderly Travel Patterns in a Smart House Using PIR Sensors and DCNN , 2018, IEEE Sensors Journal.

[19]  Daniel Austin,et al.  Time Out-of-Home and Cognitive, Physical, and Emotional Wellbeing of Older Adults: A Longitudinal Mixed Effects Model , 2015, PloS one.

[20]  William D. Kearns,et al.  Ultra wideband radio: A novel method for measuring wandering in persons with dementia , 2008 .

[21]  Diane J. Cook,et al.  Activity Discovery and Activity Recognition: A New Partnership , 2013, IEEE Transactions on Cybernetics.

[22]  Rong Chen,et al.  A Two-stage Method for Solving Multi-resident Activity Recognition in Smart Environments , 2014, Entropy.

[23]  Daniel Roggen,et al.  Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition , 2016, Sensors.

[24]  Alex Mihailidis,et al.  Autonomous Unobtrusive Detection of Mild Cognitive Impairment in Older Adults , 2015, IEEE Transactions on Biomedical Engineering.

[25]  Datong Chen,et al.  Intelligent Video Monitoring to Improve Safety of Older Persons , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[26]  Diane J. Cook,et al.  Activity recognition on streaming sensor data , 2014, Pervasive Mob. Comput..

[27]  Abdelhamid Bouchachia,et al.  Activity recognition for indoor fall detection using convolutional neural network , 2017, 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA).

[28]  Yi-Ting Chiang,et al.  Interaction models for multiple-resident activity recognition in a smart home , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[29]  Sang Min Yoon,et al.  Human activity recognition from accelerometer data using Convolutional Neural Network , 2017, 2017 IEEE International Conference on Big Data and Smart Computing (BigComp).

[30]  Tan-Hsu Tan,et al.  Front-Door Event Classification Algorithm for Elderly People Living Alone in Smart House Using Wireless Binary Sensors , 2017, IEEE Access.

[31]  Nawel Yala,et al.  Towards improving feature extraction and classification for activity recognition on streaming data , 2017, J. Ambient Intell. Humaniz. Comput..

[32]  Diane J. Cook,et al.  CASAS: A Smart Home in a Box , 2013, Computer.

[33]  Nawel Yala,et al.  Feature extraction for human activity recognition on streaming data , 2015, 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA).

[34]  S. Abbate,et al.  MIMS: A Minimally Invasive Monitoring Sensor Platform , 2012, IEEE Sensors Journal.

[35]  Chang-Di Huang,et al.  Human action recognition system for elderly and children care using three stream ConvNet , 2015, 2015 International Conference on Orange Technologies (ICOT).

[36]  J. Cohen-Mansfield,et al.  Assessment of ambulatory behavior in nursing home residents who pace or wander: a comparison of four commercially available devices. , 1997, Dementia and geriatric cognitive disorders.

[37]  Diane J. Cook,et al.  One-Class Classification-Based Real-Time Activity Error Detection in Smart Homes , 2016, IEEE Journal of Selected Topics in Signal Processing.

[38]  Subhas Chandra Mukhopadhyay,et al.  Next Generation Sensors and Systems , 2016 .

[39]  Daniel Austin,et al.  In-home walking speeds and variability trajectories associated with mild cognitive impairment , 2012, Neurology.

[40]  Fillia Makedon,et al.  Short-Term Recognition of Human Activities Using Convolutional Neural Networks , 2016, 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS).

[41]  Seungjin Choi,et al.  Multi-modal Convolutional Neural Networks for Activity Recognition , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[42]  Tahmina Zebin,et al.  Human activity recognition with inertial sensors using a deep learning approach , 2016, 2016 IEEE SENSORS.

[43]  R. Engelman.  State of the World 2013: Is Sustainability Still Possible? , 2013 .

[44]  Celia Price.  Monitoring people with dementia ‐ controlling or liberating? , 2007 .

[45]  R. Rupprecht,et al.  To live alone and to be depressed, an alarming combination for the renin–angiotensin–aldosterone-system (RAAS) , 2012, Psychoneuroendocrinology.

[46]  Shreyank N. Gowda.  Human Activity Recognition Using Combinatorial Deep Belief Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[47]  Diane J. Cook,et al.  Learning Setting-Generalized Activity Models for Smart Spaces , 2012, IEEE Intelligent Systems.

[48]  Tan-Hsu Tan,et al.  DCNN-based elderly activity recognition using binary sensors , 2017, 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA).

[49]  Ganesh R. Naik,et al.  CNN based approach for activity recognition using a wrist-worn accelerometer , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[50]  Yuqing Chen,et al.  A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.